Investigation on reduction of job shop scheduling problem in real time application using enhanced optimisation technique

Abstract

The Job Shop Scheduling issue (JSP) is a challenging NP-hard problem that can be solved using heuristics and direct procedures. Researchers focus on minimizing makespan and tardiness, with single-objective optimization aiming to find the best timetable to reduce makespan. This research uses genetic algorithms as its primary method, encoding solutions as sequences of machine assignments for every task. Using crossover, mutation, and selection operations, the genetic algorithm generates a population of candidate solutions. Another method is to apply two Virtual Component Grouping (VCG) algorithms: the earliest start time (EST) method and the shortest processing time (SPT) algorithm. The problem can be resolved using two hybrid genetic algorithms: the genetic algorithm and the EST algorithm combined in the first hybrid genetic algorithm, and the genetic algorithm and the SPT method combined in the second hybrid genetic algorithm. This approach helps to optimize the job shop scheduling process newline